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Citation - Scopus: 3
Derin Öǧrenme ile Zemin Dokusu Sınıflandırma
(IEEE, 2018) Ozuysal, Mustafa
In this study, we investigate the use of transfer learning on various deep neural network architectures pretained on the ImageNet data set for ground texture classification purposes. We introduce a new ground texture data set collected from seven different areas. We retrain deep neural network's last layer or when possible the full set of layers on this data set. The results show that it is possible to discriminate the ground textures even when very small images are used.
Parça Tabanlı Eǧitimin Evrişimli Yapay Sinir Aǧları ile Nesne Konumlandırma Üzerindeki Etkisi
(IEEE, 2017) Orhan, Semih; Bastanlar, Yalin
In recent years, Convolutional Neural Networks (CNNs) have shown great performance not only in image classification and image recognition tasks but also several tasks of computer vision. A lot of models which have different number of layers and depths, have been proposed. In this work, locations of leopards are tried to be identified by deep neural networks. To accomplish this task, two different methods are applied. First of them is training neural network using with entire images, second of them is training neural networks using with image patches which are cropped from full size of images. Patch training model has shown better performance than full size of image trained model.

Computer Engineering / Bilgisayar Mühendisliği